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Use when the user asks to "plan this feature", "plan refactor", "research & plan", "plan auth/API/work", or needs multi-step work with evidence-based planning before coding. Understands → Researches (via Local Search/Research) → Plans → Implement. No guessing; validates with code.
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.
Specification-first AI development powered by Ouroboros. Socratic questioning exposes hidden assumptions before writing code. Evolutionary loop (Interview → Seed → Execute → Evaluate → Evolve) runs until ontology converges. Ralph mode persists until verification passes — the boulder never stops. Use when user says "ralph", "ooo", "don't stop", "must complete", "until it works", "keep going", "interview me", or "stop prompting".
Design new Claude skills from structured idea specifications. Use when the skill auto-generation pipeline needs to produce a Claude CLI prompt that creates a complete skill directory (SKILL.md, references, scripts, tests) following repository conventions.
Ming Court Code —— Standardize Claude Code development processes using the institutional framework of the Ming Dynasty court. Three-level adaptive modes: Oral Edict (rapid execution), Court Debate (structured solution), Morning Court (multi-agent parallel processing).
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
Develop agentic software and multi-agent systems using Google ADK in Python
Use this skill when building AI applications with OpenAI Agents SDK for JavaScript/TypeScript. The skill covers both text-based agents and realtime voice agents, including multi-agent workflows (handoffs), tools with Zod schemas, input/output guardrails, structured outputs, streaming, human-in-the-loop patterns, and framework integrations for Cloudflare Workers, Next.js, and React. It prevents 9+ common errors including Zod schema type errors, MCP tracing failures, infinite loops, tool call failures, and schema mismatches. The skill includes comprehensive templates for all agent types, error handling patterns, and debugging strategies. Keywords: OpenAI Agents SDK, @openai/agents, @openai/agents-realtime, openai agents javascript, openai agents typescript, text agents, voice agents, realtime agents, multi-agent workflows, agent handoffs, agent tools, zod schemas agents, structured outputs agents, agent streaming, agent guardrails, input guardrails, output guardrails, human-in-the-loop, cloudflare workers agents, nextjs openai agents, react openai agents, hono agents, agent debugging, Zod schema type error, MCP tracing failure, agent infinite loop, tool call failures, schema mismatch agents
Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Covers DSPy debugging, error diagnosis, and troubleshooting.